
"""simple docstring"""






import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX




def __magic_name__	(				_lowerCamelCase:		list,					_lowerCamelCase:		list,					_lowerCamelCase:		list,					_lowerCamelCase:		list,					_lowerCamelCase:		list			)					->						float:

	'''simple docstring'''




	lowerCAmelCase												=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(_lowerCamelCase			)]			)
	lowerCAmelCase												=  np.array(_lowerCamelCase			)
	lowerCAmelCase												=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(),					_lowerCamelCase			)			),					x.transpose()			),					_lowerCamelCase			)
	return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]			)




def __magic_name__	(				_lowerCamelCase:		list,					_lowerCamelCase:		list,					_lowerCamelCase:		list			)					->						float:

	'''simple docstring'''




	lowerCAmelCase												=  (1, 2, 1)
	lowerCAmelCase												=  (1, 1, 0, 7)
	lowerCAmelCase												=  SARIMAX(
	    _lowerCamelCase,					exog=_lowerCamelCase,					order=_lowerCamelCase,					seasonal_order=_lowerCamelCase			)
	lowerCAmelCase												=  model.fit(disp=_lowerCamelCase,					maxiter=600,					method='''nm'''			)
	lowerCAmelCase												=  model_fit.predict(1,					len(_lowerCamelCase			),					exog=[test_match]			)
	return result[0]




def __magic_name__	(				_lowerCamelCase:		list,					_lowerCamelCase:		list,					_lowerCamelCase:		list			)					->						float:

	'''simple docstring'''




	lowerCAmelCase												=  SVR(kernel='''rbf''',					C=1,					gamma=0.1,					epsilon=0.1			)
	regressor.fit(_lowerCamelCase,					_lowerCamelCase			)
	lowerCAmelCase												=  regressor.predict(_lowerCamelCase			)
	return y_pred[0]




def __magic_name__	(				_lowerCamelCase:		list			)					->						float:

	'''simple docstring'''




	train_user.sort()
	lowerCAmelCase												=  np.percentile(_lowerCamelCase,					25			)
	lowerCAmelCase												=  np.percentile(_lowerCamelCase,					75			)
	lowerCAmelCase												=  qa - qa
	lowerCAmelCase												=  qa - (iqr * 0.1)
	return low_lim




def __magic_name__	(				_lowerCamelCase:		list,					_lowerCamelCase:		float			)					->						bool:

	'''simple docstring'''




	lowerCAmelCase												=  0
	lowerCAmelCase												=  0
	for i in list_vote:
		if i > actual_result:
			lowerCAmelCase												=  not_safe + 1
		else:
			if abs(abs(_lowerCamelCase			) - abs(_lowerCamelCase			)			) <= 0.1:
				safe += 1
			else:
				not_safe += 1
	return safe > not_safe


if __name__ == "__main__":
					# data_input_df = pd.read_csv("ex_data.csv", header=None)
					UpperCAmelCase        =				[[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
					UpperCAmelCase        =				pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					UpperCAmelCase        =				Normalizer().fit_transform(data_input_df.values)
					# split data
					UpperCAmelCase        =				normalize_df[:, 2].tolist()
					UpperCAmelCase        =				normalize_df[:, 0].tolist()
					UpperCAmelCase        =				normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					UpperCAmelCase        =				normalize_df[:, [1, 2]].tolist()
					UpperCAmelCase        =				x[: len(x) - 1]
					UpperCAmelCase        =				x[len(x) - 1 :]

					# for linear regression & sarimax
					UpperCAmelCase        =				total_date[: len(total_date) - 1]
					UpperCAmelCase        =				total_user[: len(total_user) - 1]
					UpperCAmelCase        =				total_match[: len(total_match) - 1]

					UpperCAmelCase        =				total_date[len(total_date) - 1 :]
					UpperCAmelCase        =				total_user[len(total_user) - 1 :]
					UpperCAmelCase        =				total_match[len(total_match) - 1 :]

					# voting system with forecasting
					UpperCAmelCase        =				[
					    linear_regression_prediction(
					        trn_date, trn_user, trn_match, tst_date, tst_match
					    ),
					    sarimax_predictor(trn_user, trn_match, tst_match),
					    support_vector_regressor(x_train, x_test, trn_user),
					]

					# check the safety of today's data
					UpperCAmelCase        =				"""""" if data_safety_checker(res_vote, tst_user) else """not """
					print("""Today's data is {not_str}safe.""")




"""simple docstring"""






import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar



class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  logging.get_logger()

				# the current default level is logging.WARNING
				lowerCAmelCase												=  logging.get_verbosity()

				logging.set_verbosity_error()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_warning()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_info()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_debug()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  logging.get_verbosity()

				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				lowerCAmelCase												=  '''Testing 1, 2, 3'''

				# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
				if level_origin <= logging.WARNING:
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,msg + '''\n'''			)

				# this is setting the level for all of `transformers.*` loggers
				logging.set_verbosity_error()

				# should not be able to log warnings
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_			)
				self.assertEqual(cl.out      ,''''''			)

				# should be able to log warnings again
				logging.set_verbosity_warning()
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_			)
				self.assertEqual(cl.out      ,msg + '''\n'''			)

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_			)






			@mockenv(TRANSFORMERS_VERBOSITY='''error'''			)
			def 					UpperCAmelCase							(self  :	Any			)       ->	str:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()
				# this action activates the env var
				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)

				lowerCAmelCase												=  os.getenv('''TRANSFORMERS_VERBOSITY'''      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  logging.log_levels[env_level_str]

				lowerCAmelCase												=  logging.get_verbosity()
				self.assertEqual(
				    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,F"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""      ,)

				# restore to the original level
				lowerCAmelCase												=  ''''''
				transformers.utils.logging._reset_library_root_logger()






			@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''			)
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Tuple:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()
				lowerCAmelCase												=  logging.logging.getLogger()
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					# this action activates the env var
					logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''      ,cl.out			)

				# no need to restore as nothing was changed






			def 					UpperCAmelCase							(self  :	Dict			)       ->	Any:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()

				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				lowerCAmelCase												=  '''Testing 1, 2, 3'''

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''			):
					# nothing should be logged as env var disables this method
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,''''''			)

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''			):
					# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,msg + '''\n'''			)





def __magic_name__	(				)					->						List[Any]:

	'''simple docstring'''




	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()


